Journal of Research in Personality 43 (2009) 127–136
Contents lists available at ScienceDirect
Journal of Research in Personality
journal homepage: www.elsevier.com/locate/jrp
A componential model of situation effects, person effects, and
situation-by-person interaction effects on social behavior
Joachim I. Krueger *
Department of Psychology, Brown University, Box 1853, Providence, RI 02912, United States
a r t i c l e
i n f o
Article history:
Available online 23 January 2009
Keywords:
Social behavior
Situationism
Dispositionism
Automaticity
a b s t r a c t
Social and personality psychology seek to understand the regularities of human behavior in social context
and to separate critical causal variables assumed to reside in the social situation, in the person, and in the
interaction between the two. A componential model is presented, which assumes that the three types of
effect are theoretically and conceptually independent, although they may be confounded in practice. A
review of past theory and research suggests that many social and personality psychologists have misconstrued situation and person effects as competing for a limited pool of behavioral variance. A conceptual
re-orientation may overcome the limitations of both radical situationism and defensive dispositionism.
Ó 2009 Elsevier Inc. All rights reserved.
1. Introduction
‘‘Every psychological event depends upon the state of the person and at the same time on the environment, although their
relative importance is different in different cases (Lewin,
1936, p. 12).”
‘‘The question of whether individual differences or situations
are more important is an empty one that has no general
answer (Mischel, 1977, p. 340).”
Following Kurt Lewin’s writings on the dual origins of human
behavior, many social and personality psychologists assume that
behavior depends on forces inherent in the situation and on forces
residing within the person. It is also commonly assumed that there
is a fixed amount of behavioral variance. If one type of effect is
strong, the other would have been small. Representing this hydraulic view of behavioral causation, Bargh (2007) asserted that ‘‘a central focus of contemporary social psychology has been the relative
influence of external (i.e., environmental, situational) versus internal (i.e., personality, attitudes) forces in determining social judgment and social behavior” (p. 555, emphasis in the original). This
competitive construction of situation versus person effects ignores
the possibility that they might interact, a notion that Lewin took
seriously. ‘‘We seek the ‘‘cause” of events [. . .] in the relationship
between an object and its surroundings” (Lewin, 1936, p. 11).
After Mischel’s (1968) critique of trait psychology, many research programs in social and personality psychology dedicated
* Fax: +1 401 863 1300.
E-mail address: Joachim_Krueger@Brown.edu
URL: http://research.brown.edu/research/profile.php?id=10378.
0092-6566/$ - see front matter Ó 2009 Elsevier Inc. All rights reserved.
doi:10.1016/j.jrp.2008.12.042
themselves, respectively, to demonstrations of situation and person effects. This division of labor and the interdisciplinary competition it entailed hampered progress in psychology. In this article, I
focus on traditional and modern forms of the situationist thesis as
it appears social psychology, although my analysis extends to some
limitations of theories championing an exclusive focus on personality traits and personal causation. To help reform current research
practice, I sketch a componential model that separates situation effects, person effects and interaction effects. I revisit some classic
research findings to highlight how research designs can both reveal
and obscure causes of behavior. Then, I explore some implications
of the model for a contemporary expression of the situationist paradigm, namely the project to detect the automatic genesis of all social behavior. I conclude with some ideas as to why the doctrine of
situationism has proven so resilient to critique.
2. The pure componential model
Cronbach (1955) pioneered componential modeling when he
decomposed global similarity scores (see also Cronbach & Gleser,
1953). For two sets of N observations ai and bi, the sum of the
P
squared differences, (ai bi)2, is an index of overall dissimilarity.
This index may be large because there is a large mean difference,
Ma Mb, because there is a low correlation between the two sets,
ra,b, or because the two sets have different variances, va/vb – 1.
These three components of dissimilarity are differences in elevation, profile similarity and dispersion, respectively. Cronbach noted
that the overall sum index conflates these components. Studies can
yield similar dissimilarity indices, although the underlying data are
entirely different.
Consider the implications of the componential analysis for the
study of social behavior. Let ‘N’ designate a sample of individuals
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J.I. Krueger / Journal of Research in Personality 43 (2009) 127–136
and ‘a’ and ‘b’ designate two situations, in which a critical behavior
is observed. The first component, the difference in elevation,
Ma Mb, shows whether the target behavior is stronger in one situation than in the other. In social psychological research, the twosample analysis of variance, ANOVA, is the canonical tool for the
evaluation of mean differences. The successful detection of a significant difference affords the desired inference that the situation
caused a change in the persons’ average behavior.
The second component, profile similarity, ra,b, shows whether
the individual differences in behavior are consistent across situations. The correlation over respondents is thus an estimate of the
person effect. In personality research practice, it is these correlations that legitimize trait theories and afford inferences about personal causation.
The third component is the variance differential, va/vb, which
shows whether situation and person effects interact. A difference
in variance means that one situation homogenizes (or heterogenizes) behavior relative to another. Although both social and personality psychology recognize the conceptual importance of the
variance differential as an indicator of the difference between
strong (i.e., low variance) and weak (i.e., large variance) situations
(Price & Bouffard, 1974), standard research practice treats a nonone variance ratio as a nuisance. ANOVA demands equal variances
for the comparison of means, and Pearson’s product-moment correlation coefficient corrects for—and thus hides—any variance
inequality.1
The componential model accords equal theoretical significance
to the three types of effect. With S, P, and S P being theoretically
independent, eight possible combinations of effects must be considered. They are listed in Table 1. The first four cases allow clear
inferences. In Case 1, the mean behavior is the same in both situations, individual differences are uncorrelated, and the variances are
the same. Such a study is difficult to publish. Case 2 represents a
pure pattern supporting the situationist hypothesis. The mean
behavior is different, individual differences are uncorrelated, and
the variance ratio is near 1. Case 3 represents a pure pattern supporting the dispositionist hypothesis. The mean behavior is the
same, individual differences are highly correlated, and the variance
ratio is 1. Case 4 represents a pure interaction effect. The mean
behavior is the same, individual differences are uncorrelated, but
the variance ratio is skewed.
Now consider the next four cases, which involve more than one
significant component. Case 5 shows that situation and person effects can co-exist. A difference in elevation can easily be paired
with strong profile similarity. This case refutes the idea that the
relationship between S and P effects is intrinsically hydraulic (Funder & Colvin, 1991). Case 6 is central for the re-evaluation of classic
findings in social psychology. There is a main effect of S, no effect of
P, and an interaction between S and P. This pattern is a common,
even desired, outcome when a control condition is set up to yield
a low level of behavior coupled with little variance, and an experimental condition is set up to yield both, a higher elevation and a
greater variance of behavior. Case 7 appears to support the dispositionist hypothesis, although a focus on profile similarity would
miss the fact that the situations contribute to behavior by moderating the magnitude of individual differences. Finally, Case 8 shows
that all three effects can occur jointly.
Although they emphasize different sources of behavioral variance, social and personality psychologists share the view that situ-
1
In a two-sample ANOVA an interaction term can be extracted by averaging each
respondent’s data over situations, sort them by median split, and compare the means
obtained in the two situations separately for the high and the low scorers. This
method reduces to the interaction specified by the componential model. If the
situation effect is different in size, or even in direction, for the high scorers and the
low scorers, the overall variance within the two situations is different.
Table 1
The pure componential model.
Case
Situation
Person
Interaction
1.
2.
3.
4.
5.
6.
7.
8.
+
+
+
+
+
+
+
+
+
+
+
+
‘Wash-out’
‘Situation only’
‘Disposition only’
‘Interaction only’
‘Non-hydraulic’
‘Classic social’
‘Classic trait’
‘Complexity’
Note: the operator ‘‘+” indicates that the effect is present; the operator ‘‘‘‘indicates
that the effect is absent.
ation effects be construed as mean differences and person effects
as correlations. To represent these choices, the componential model has to take the form outlined above. Of course, research can be
designed so that mean differences represent person effects and
profile correlations represent situation effects. Brunswik (1955)
advised that tasks (i.e., situations) be sampled representatively
from a person’s ecological niche (Dhami, Hertwig, & Hoffrage,
2004). In this approach, a difference in variance indicates that
the behavior of one person is more variable over situations than
the behavior of another (Fleeson, 2007). For reasons the present
analysis aims to help illuminate, the ecological paradigm is underutilized in current research practice. Experimental social psychologists, whose epistemology tells them to detect subtle situation
effects, will manipulate situations and sample persons rather than
vice versa (Wells & Windschitl, 1999). Likewise, personologists,
whose epistemology tells them to demonstrate the stability of differences over multiple individuals, will also prefer to sample many
respondents and limit their analysis to few situations.
3. The constrained model
Ordinary research practice imposes several constraints on the
theoretical independence of effects. One type of constraint is
scale-end effects. The total absence of a behavior imposes a floor
effect, and organismic limitations set a ceiling for how often or
how strongly a person can act. Therefore, a large situation effect reduces the variance across observations. If all observations are 1 and
7 on a Likert scale, respectively, for the control and the experimental condition, no person effect and no interaction can occur. Conversely, if individual scores vary across the entire range of the
scale in both conditions, the person effect is large and no situation
or interaction can occur. Yet, there is an asymmetry of constraint. A
large person effect does not limit the other two effects as much as
the situation effect does. It is possible to find ra,b = 1 while
Ma Mb > 0 and while va/vb – 1. In short, situation and person effect may appear to be compensatory because of the limitations of
measurement.
A strong interaction effect has asymmetric implications for the
two main effects. If the mean response in one condition is near one
of the endpoints of the scale, and if variation around that mean is
small, the mere increase in response variance in the other condition entails not only an interaction effect, but also a spurious main
effect of the situation. Recall the significance of Case 6 for classic
social psychological work and take Milgram’s (1963) obedience
study as an example. In the control condition, where no social
influence was brought to bear, no one gave the maximum shock.
Both the mean level of the critical behavior and its variance were
zero. In contrast, participants under the experimenter’s influence
were more punitive on average and their behavior was more variable. Because an increase in the variance of behavior could occur
only against the background of the floor effect in the control condition, the interaction effect also produced a main effect of situa-
J.I. Krueger / Journal of Research in Personality 43 (2009) 127–136
tion. It is as legitimate to say that Milgram found an interaction effect as it is to say that he found a situation effect.2 In contrast, if an
interaction effect is observed because the variance of observations is
particularly small in one condition, an overall person effect less
likely. If differences in variance militate against high correlations, a
significant person effect is more impressive in the presence of an
interaction than in the absence of it. If all three effects are obtained
(i.e., Case 8), and if the effect size of S and P are the same, the latter is
more compelling than the former.
The asymmetric constraints the three effects impose on one another complicate the interpretation of empirical results. The arbitrariness of these constraints is further highlighted by the fact
that the asymmetry is inverted in research using ecological sampling. When situations are sampled instead of respondents, a large
person effect constrains the situation effect more strongly than
vice versa, and a large interaction is more likely to entail a spurious
person effect than a spurious situation effect.
The componential model is psychometric in origin but experimental in application. This discrepancy leads to an additional constraint. Whereas ANOVA assumes the same variance across
conditions, the componential model identifies differences in variance as the reflection of interaction effects. Whereas ANOVA casts
effects in standardized form, the componential model retains the
raw form. Standardized situation effects are large to the extent that
individual differences and error variance are small. Thus, standardization feeds into the assumption that situation and person effects
are compensatory. If a significant situation effect is found, it must
have overwhelmed the person effect. This conclusion has no merit
in a within-subjects design. As the correlation between individual
differences increases, tests of mean differences become more powerful, and standardized effects become larger. There is no comparable standardization of the person effect that provides a test of
significance. Inasmuch as correlation coefficients between individual differences measure are corrected for unreliability, they only
subtract error variance, but not situational variance.
The realities of behavioral measurement can obscure the conceptual independence of the causes of behavior. In research practice, situation and person effects can appear to be opposites.
Nonetheless, the onus is on researchers to recognize and overcome
these constraints; the onus is not on the model to reify measurement constraints as true phenomena. Current research practice
has a long way to go to meet this goal. In social psychology, the
standard between-respondent design does not even allow the
simultaneous estimation of person and interaction effects.
Whereas a full componential analysis requires a repeated-measures design, many social psychologists prefer between-respondent designs, perhaps because of the field’s tradition of deceiving
participants. If the purpose of an experiment must be concealed,
participants cannot serve as their own controls.
Measures of individual differences are not integral to the design
of the typical social psychological study; instead, they are tacked
on. Respondents in each condition complete scales assessing individual differences in attitudes or traits that the investigators think
may be associated with differences in the critical behavior (Snyder
& Ickes, 1985). For example, some studies designed to test the effects of social influence on compliance also measure individual differences in, for instance, authoritarianism or self-monitoring. This
is a weak strategy. It seeks to detect a correlation between a general trait and a specific behavior (Fishbein & Ajzen, 1975).
Another strategy is to infer person effects without even measuring them. Here, researchers evaluate the respondents’ behavior
against baseline assumptions. When social influence is designed
2
According to one school of thought, interaction effects supersede main effects in
ANOVA when both are statistically significant (Keppel, 1991).
129
to get people to do what they ordinarily would not do (i.e., to comply with a request that violates self-interest), participants in control conditions do as they please, namely nothing. Hence, their
behavior (or rather lack thereof) is considered an expression of
their disposition. Behavior in the experimental condition is not
only attributed to the power of the situation, but also to the person’s weakness to resist it (Sabini & Silver, 2005). In the spirit of
this argument, Bargh (2007, p. 556) asserted that Milgram ‘‘demonstrated the power of a situational influence (i.e., the experimenter’s authority) over the subject’s behavior to override
presumed internal influences (i.e., the subject’s presumed personal
values not to cause pain or harm another)”.
4. Classic situationism: the alleged hydraulics of misbehavior
After decades of research that did not properly separate situation, person, and interaction effects, a strong situationist attitude
prevails in social psychology. Bargh (2007, p. 555) concludes
‘‘many of the classic findings in the field—such as Milgram’s obedience research, Asch’s conformity studies, and Zimbardo’s mockprison experiment—seemed to indicate that the external forces
swamped the internal forces when the chips were down”. If so,
any belief in personal causation must be a folk psychological
superstition (Ross & Nisbett, 1991). Laypeople—and personality
psychologists—who cleave to dispositional inferences must be
committing a ‘‘fundamental attribution error”. If they only understood the primacy of situations, their attributions would not only
be more in line with scientific norms, but they would also be more
‘‘charitable” (Iyengar, Lepper, & Ross, 1999).
Un momento, por favor. As we have already seen, experimental
research has its own biases and limitations. When participants
are randomly assigned to conditions and significant statistical results are privileged, the evidence for situational causes accumulates faster than evidence for personal causes. Experimental
research provides opportunities for large situation effects because
the independent variables are specifically designed to change
behavior. In contrast, extraneous individual differences have no
advantage by design. Despite this tilted playing field, the empirical effect sizes of situations are no greater than the effect of persons (.2 < r < .3; Bowers, 1973; Funder & Ozer, 1983; Richard,
Bond, & Stokes-Zoota, 2003). Nonetheless, situation effects garner
more respect because they spring from ‘‘apparently minor but
important details of the situation” (Ross & Nisbett, 1991, p. 10),
or because ‘‘the preservation of objectivity in psychology depends
upon the observability of truly causal variables” (Bowers, 1973, p.
308).
Experimental social psychology seeks to buttress the primacy of
situational causes by exploiting its own charge against folk psychology and its footprint, the fundamental attribution error. Not
only are the observed situation effects introduced as evidence,
but also people’s inability to foresee these effects. Milgram
(1974, p. 205) suggested that ‘‘the disposition a person brings to
the experiment is probably less important a cause of his behavior
than most readers assume”. To corroborate this rhetoric, one
would have to show that experimental findings are more valid
when they differ from folk beliefs than when they do not. Logically,
there is no such relationship. Other sciences (e.g., astronomy, evolutionary biology) fought historic battles to rectify naïve beliefs,
but their findings are not considered more valid because they differ
from popular misconceptions. The inverse implication of anchoring
the interpretation of experimental findings on their relationship to
folk beliefs is also invalid. There is no reason to think that if scientific data conform to such beliefs, they have no value. Other disciplines do not endorse this vulgar form of Bayesianism. Indeed,
personality researchers dedicated to the trait perspective, seem
to want to confirm folk ideas of dispositionism.
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J.I. Krueger / Journal of Research in Personality 43 (2009) 127–136
A related argument for the primacy of situational causes is that
the situations that affect people’s behavior in the laboratory—and
in real-world settings that the experiments seek to model—are
‘‘strong” situations. Strong situations ‘‘prompt similar responses
in almost everyone, whereas weak situations will evoke greater
variation” (Suls & David, 1996, p. 1002; see alsoLissek, Pine, & Grillon, 2005).3 My old sociology professor Heinz Harbach used to say,
‘You can sing hymns in a bar, but you cannot drink beer in church.’
Kenrick and Funder (1988) make the same point when they distinguish between picnics and funerals. The latter tightly regulate
behavior by making salient social norms, customs, or rituals; the
former make salient the individual’s freedom to do as they please,
and different individuals please differently.
According to the situationist interpretation, the difference in
variance signals a situation effect, and thus a hydraulic depletion
of the person effect. Even Mischel (1977, p. 347), otherwise known
for his interactionist views, suggested that ‘‘strong situations shift
the cause of behavior from a dispositional locus to a situational
one”. In contrast, the componential model casts the difference between low-variance and high-variance situations as a situation-byperson interaction. What matters according to the componential
model is the variance in one situation relative to the variance in another. There is no absolute categorization of a situation as being
either strong or weak.4
Consider Milgram’s research. Intuition suggests that it is the
presence of an insistent authority figure that constitutes the
strength of the situation. It is the influence of the authority that
steers participants away from doing what they want to do, namely
nothing. By this logic, the control condition is weak because participants are on their own. Notice that this view is opposite to the statistical definition of a strong situation. It is the control condition
that yields low behavioral variance and the experimental condition
that yields high variance. The mind balks at the idea that Milgram’s
pressure on his participants to shock their fellow man to death was
a weak situation. And yet, statistically it was.
Asch’s (1956) conformity studies allow a similar interpretation.
In the control condition, virtually no one offered an incorrect response when deciding which of three lines matched the target line
in length. Hence, the control condition was strong by the variance
criterion. The experimental conditions, in which three or more
confederates offered unanimous but false decisions, about one
third of the responses was conforming and false. These responses
were more variable and less predictable, thus implying that the
experimental condition was statistically weak.
Now consider the notorious Stanford Prison Experience (Haney,
Banks, & Zimbardo, 1973). As there was no distinction between a
control and an experimental condition, no relative distinction between strong and weak situations could be drawn. Yet, according
to the master narrative, both guards and prisoners found themselves in a strong situation. Nonetheless, the study’s architect reports considerable individual differences within both groups
(Zimbardo, 2007). Some guards appeared to relish their power,
whereas others attempted not to escalate the intergroup conflict;
some prisoner’s rebelled or got sick, whereas others submitted or
withdrew psychologically (Carnahan & McFarland, 2007; Krueger,
2008). Inasmuch as the prison setting brought out these unexpected individual differences, the situation was weak.
A more complex picture emerges from Darley and Latané’s
(1968) study on the bystander effect. On the one hand, it was the
control condition that was statistically strong. Knowing that the
3
An ecological study yields distinctions between ‘‘strong” people who act very
much the same across situations, and ‘‘weak” people who respond to varying
situational demands.
4
Fleeson’s (2007, p. 829) proposal that ‘‘[Strong] situations are predictors of
variability in personality states” is consistent with the present analysis.
victim’s welfare depended on them alone, most (85%) participants
helped. When the group of potential helpers increased in size, the
probability of helping decreased. With a group size of five, fewer
participants (31%) intervened. Thus, a large group also homogenized behavior, though with a different outcome. With an intermediate group size of two, the behavior of the individual was least
predictable (62% helped), and hence the situation was the weakest.
In other words, a linear increase in the strength of the independent
variable yielded a curvilinear trajectory in the statistical strength
of the situation.
Whereas the situationist project pits strong situations against
strawman dispositions, the componential model allows that situations activate multiple dispositions (Sabini, Siepmann, & Stein,
2001). In Milgram’s (1963) study, the experimenter’s behavior
was designed to activate a norm to respect legitimate authority
and a willingness to honor a contract demanding cooperation. At
the same time, the victim’s protests activated the norm to do no
harm. As the experiment progressed, the activation of both norms
grew stronger, resulting in pressure to resolve a ‘‘crisis of conscience”. The experience of such a crisis is a fundamentally psychological phenomenon rather than an environmental one, and its
resolution reveals the relative strength of distinctive personal
dispositions.
To illustrate, suppose there is a symmetrical bivariate distribution of independent individual differences in respect for the two
norms. For each disposition, weak and strong norm endorsers are
separated by median split, which yields four types of participant.
The upper right quadrant of Fig. 1 (top) comprises individuals
who experience the classic crisis of conscience because they endorse both norms. These participants cannot resolve the crisis
without privileging one norm over the other. An individual’s decision to yield may not be predictable (i.e., p = .5). The lower right
quadrant comprises individuals who care about obedience but
not about altruism (here, p = 1). These individuals yield without
conflict. Participants in the lower left quadrant are complacent,
not caring either way. These individuals may decide by tossing a
mental coin, thus ending up with the same intermediate probability of yielding that characterizes individuals in conflict (again,
p = .5). Finally, participants in the upper left quadrant are altruists
who are immune to obedience pressures. They resist without conflict (now, p = 0). Each of these for probabilities of yielding is conditioned on the level of dispositional obedience (high or low) and
the level of dispositional altruism (high or low).
The probabilities presented in the margins are conditioned on
the level of only one disposition. The probability of yielding given
a high disposition of obedience or given a low level of altruism is
.75 ([.5 + 1]/2). Finally, as the four combinations of dispositions
are assumed to be equiprobable, the unconditional probability of
yielding for each is computed as the product of the dually conditional probability and the two base rates. These unconditional
probabilities are presented in brackets. In this example, the situation effect is undefined because there is no comparison between
situations. In contrast, the two independent and additive disposition effects account for some of the behavioral variance. The traits
of obedience and altruism predict yielding, respectively, with
U = .5 and .5.
To introduce a situation effect, a second scenario needs to be
considered. Consider an experimental situation, in which the traits
of obedience and altruism are activated independently and, respectively, in 70% and 30% of the respondents (Fig. 1, bottom). This
arrangement gratifies the situationist because the overall probability of yielding is now .2 higher than in it was in the first scenario.
Yet, the pattern does not defeat the dispositionist. The traits of obedience and altruism now predict yielding and resisting, respectively, with U = .7. The simultaneous emergence of a situation
effect and an increased disposition effect may seem paradoxical
J.I. Krueger / Journal of Research in Personality 43 (2009) 127–136
p(Y|O) = .75
Resist
0
[0]
Crisis
.5
[.125]
p(Y|A) = .25
Altruism
p(Y|~O) = .25
131
Random Choice
.5
[.125]
Yield
1
[.25]
p(Y|~A) = .75
Obedience
p(Y|~O) = .15
p(Y|O) = .85
Crisis
.5
[.105]
p(Y|A) = .15
Altruism
Resist
0
[0]
Random Choice
.5
[.105]
Yield
1
[.49]
pY|(~A) = .85
Obedience
The letters Y, O, ~O, A, and ~A, respectively refer to the behavior of yielding and to high and
low scores on the traits of obedience and altruism. In the margins, the probability of Y is
conditioned on one trait; in each quadrant, the probability of Y (above the brackets) is
conditioned on both traits (e.g., p(Y|O ∩ A). The probability in brackets is the product of this
conditional probability and the base rates of the traits.
Fig. 1. Behavior as a result of two dispositions.
if one expects the two to be negatively related. Yet, such a pattern
not only corroborates the conceptual separation of situation and
person effects, but it is also empirically plausible.
5. Contemporary situationism: l’insoutenable automaticité de
l’être
Despite repeated challenges, situationism thrives in contemporary social psychology. Indeed, the influential automaticity paradigm takes situationism to its logical conclusion. Work in this
paradigm seeks to demonstrate that behavior can be elicited without the involvement of higher mental processes on the part of the
respondent. Automaticity effects are pure when people are unaware of the operative external stimuli, unaware of their own processing of these stimuli, and unaware of the changes in their
own responses. The automaticity paradigm has its roots in the doctrine of suggestion (Kihlstrom, 2007). From the French hypnotiseurs
Charcot, Bernheim, and Janet, to Freud and the psychoanalysts, to
early social psychologists such as Lorge and Asch, the art and science of social influence was expressed by an agent’s ability to over-
come a target’s critical faculties to change his or her perception,
belief, affect, or behavior.
In the automaticity paradigm, subliminal primes are the vehicles of suggestion. And it works. When primes directly affect
behavior, strategic mental processes are rendered irrelevant (as
in studies on ideomotor action; Knuf, Aschersleben, & Prinz,
2001), or they are subjugated by the external stimuli (as in studies
on ‘‘auto-motives”, Bargh, Gollwitzer, Lee-Chai, Barndollar, & Trotschel, 2001). Bargh (2007, p. 557) reports that ‘‘even in the domain
of self-regulation research [. . .] complex goal pursuit can be put
into motion by situational features instead of exclusively by consciously made intentions and choices”. According to this view,
higher-order mental processes cease to make any genuine contribution to behavior if they can be engaged or disabled by external
stimuli.
If the object of classic work on social influence was to slay a dispositionist folk psychology, the object of contemporary studies on
the social unconscious is to slay a mentalistic folk psychology. The
project begins by ‘‘assuming a central role of conscious (intentional, effortful, and aware) choice and monitoring processes. Re-
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J.I. Krueger / Journal of Research in Personality 43 (2009) 127–136
search then has the effect of discovering the extent and role of nonconscious components of the process or phenomenon” (Bargh,
2007, p. 556, emphasis in the original). By assuming compensatory
causes of behavior, any positive evidence for the strength of one
entails a reduction of the other. The success of such eliminative situationism is measured by the speed with which the black box is
being emptied.
Proponents of the automaticity project have become increasingly assertive about their debt to behaviorism (Bargh & Chartrand,
1999; Bargh & Ferguson, 2000). There is, however, a difference.
Behaviorists placed mental constructs outside of the scientific domain, postulating that ‘‘science ultimately explains behavior in
terms of ‘‘causes” or conditions which lie beyond the individual
himself” (Skinner, 1955–1956, p. 47). In contrast, automaticity
researchers believe that ‘‘the social psychological study of nonconscious judgment and behavioral phenomena adds to our understanding of the purpose of conscious processes” (Bargh, 2007, p.
557). Much like the earlier students of social influence needed dispositional causes of behavior as foils for situationism, so do present-day students of automaticity need the hypothesis of
conscious behavioral control as an idea to be refuted (Wegner,
2002). Ultimately, the doctrine of automaticity becomes the victim
of its own success. It cannot afford to destroy the idea of consciousness because that would render any further demonstration of automaticity pointless. Given this paradox, Bargh’s (2007) summation
is predictably anticlimactic. ‘‘This is not to say that consciousness
does not exist or is merely an epiphenomenon (but that) the purpose of consciousness (i.e., why it evolved) probably lies elsewhere
(p. 565).” Presumably, consciousness allows people to cope with
new situations until their behavior is routinized. Notice how well
this logic applies to the classic studies on social influence. The crises of conscience, which Asch, Milgram, and others had so carefully
orchestrated, forced many distressed participants into active and
effortful mentation. Consciousness was called upon to solve an
intellectual, emotional, and moral dilemma. Yet, proponents of
the automaticity paradigm deny consciousness even this fire fighter role. In The Handbook of Social Psychology (4th ed.), Wegner and
Bargh (1998) maintained that conformity and obedience are blind.
Consciousness has been made a laughing-stock. ‘‘Where oh where,
in all of these findings, was the internal, intentional, rational control of one’s own behavior?” (Bargh, 2007, p. 556).
The self-satisfied glow of success of l’automaticité est vraiement
insoutenable. Tests of automaticity are pronounced successful if a
prime, or some other subtle manipulation, has a statistically significant effect on behavior (Krueger, 2001). From the rejection of the
null hypothesis it is then inferred that conscious processes did little to counteract the automatic effect. This inference is illogical.
Even if the hydraulic principle were granted, one would have to
estimate how large the automatic effect would have been in the
absence of counteracting effects before one could estimate how
small the effect of consciousness was.
Bargh (2007) entertains the idea of unfettered situationism in
his review of Lhermitte’s (1986) neuro-psychological findings.
Working at the Salpêtrière like Charcot before him, Lhermitte
(1986) studied patients with ablated frontal lobes. Exposing them
to various suggestive social settings, he found that these patients
spontaneously behaved in ways that were consistent with the situations’ affordances. When presented with a revolver, they would
load and point it; when taken out to a garden, they would water
the plants; when ushered into a bedroom, they would put on pajamas and go to sleep. Lhermitte coined the term ‘‘environmental
dependency syndrome” to characterize this finding, saying that
the patients’ ‘‘decision for their actions was not one they made
themselves” (Lhermitte, 1986, p. 342).
The environmental dependency syndrome is situationism writ
large. With the math of subtraction, Bargh (2007, p. 564) concludes
that ‘‘the behavior of these patients became continually driven by
cues of the environment and by little else”. Yet, two critical features of Lhermitte’s findings suggest a different interpreation. First,
the environmental dependency syndrome sets a ceiling for situation effects. The situation effects among normal participants are
much smaller, and the question is what psychological mechanisms
limit their size. Situation effects may be statistically significant, but
how large are they compared with the ceiling of pure, unfettered
situationism? In other words, Bargh performed the wrong kind of
subtraction.
Second, and this point was not lost on Lhermitte, even the massive effects of environmental dependency did not nullify dispositional causes. As the componential model suggests, situation and
person effects can co-occur. In some situations patients with different personal backgrounds responded to different affordances of the
same situation. A patient with a privileged social background acted
like a guest at a buffet, whereas another patient with a more humble background acted like a server. The former acted like a physician in the doctor’s office, whereas the latter acted like a nurse.
Lhermitte (1986, p. 342) concluded that ‘‘the patients’ personality
formed an integral part of their EDS (environmental dependency
syndrome) and gave it an individual aspect”. Social situations
rarely make a single unambiguous demand on the person, but
the person extracts or constructs meaning depending on his or
her prior history. This is as true in social perception as it is in nonsocial perception (Helmholtz, 1867; Kersten, Mamassian, & Yuille,
2004; Rock, 1983), Lhermitte (1986, p. 335) understood that ‘‘the
shifting balance between dependence and independence with relation to the environment is one of the basic components of personal
autonomy” (p. 335).
6. Self-regulation and strategic behavior
In the hands of the situationists, the person becomes an incredible vanishing act (Krueger, 2007; Krueger & DiDonato, 2005).
According to the componential model, this need not be so, but
being a statistical device, the model does not supply a substantive
defense of person and interactional effects. Suitable defenses can
be found in psychological theory. Since the time of ‘‘It-is-not-thesituation-but-what-you-make-of-it” Epictetus, self-regulation theorists have maintained that individuals creatively contribute to
their own behavior. Carver and Scheier (1998) suggest that selfregulation arises from a cybernetic interplay among an individuals’
goals, the monitoring of goal-reality discrepancies, and corrective
processes. Bandura (1997) regards situational and personal causes
as embedded in a framework of reciprocal determinism. Higgins
emphasizes regulatory fit of an individual’s promotion versus prevention focus with situational affordances (Higgins & Spiegel,
2004). The interactionism espoused by these models is dynamic
rather than mechanistic (Endler & Parker, 1992), and thus goes beyond how interactions are modeled by the componential model
and the non-interactionist theories the componential model seeks
to replace.
Strategic behavior is a compelling kind of self-regulation as it
also involves the regulation of others (von Neumann & Morgenstern, 1944). Consider two examples of how strategic behavior
can create situation-by-person interactions. The first example involves the deliberate generation of unpredictability. Nozick
(2001) pointed out that it is often not in a person’s best interest
to be perfectly predictable (see also Dunbar, 2003). A perfectly predictable person is vulnerable to exploitation and unable to take
advantage of others. A player with a low threshold for cooperation
in prisoner’s dilemma risks being suckered; a poker player who
never bluffs, or who bluffs all the time, courts financial ruin. At
minimum, a perfectly predictable person is boring. The optimum
lies somewhere between perfect predictability and perfect unpre-
J.I. Krueger / Journal of Research in Personality 43 (2009) 127–136
dictability, and no one will be bored trying to find where exactly
that optimum lies. Self-regulation affords self-improvement, and
Nozick (2001, p. 296) had an interactionist recommendation.
‘‘What a person might most want or benefit from is to be reliably
predictable in situations in which his best action is to cooperate
to mutual benefit, and to be largely unpredictable in situations of
conflict of interest.”
At its most sophisticated, strategic reasoning creates constraints and control. The oldest example is also the best. Odysseus
instructed his shipmates to tie him to the mast so that he could
hear the sirens’ song and live. He knew that he would not be able
to resist their call, and he knew that his mates would obey him if
they heard him demanding to be untied. That is why he had them
plug their ears—and to keep them from jumping overboard themselves. His foreknowledge of the power of the situation was perhaps determined by his wisdom, experience, or his genetic
constitution. Yet, it was phenomenally strategic and rational (Elster, 2000; Schelling, 1978, 1984). From a game-theoretic perspective, Odysseus was divided against himself. Knowing that when
hearing the sirens’s song he would prefer to yield, choosing to
be strong in the present ensured that both, his present and his future self, obtained their second-best outcome (see the combined
preference matrix in Fig. 2). Being strong now was the best
self-regulatory move given knowledge of his future weakness
(Brams, 1994).
The emergence of conscious self-regulation was a major event
in the evolution of mind. Jaynes (1976) provocatively suggested
that there was a qualitative shift from the bicameral minds of the
Illiad and the early books of the Hebrew Bible to the self-conscious
and self-regulatory minds of the Odyssey and the later books of the
Bible. Strict situationism and the automaticity paradigm suggest
that this shift was, and indeed any kind of self-conscious strategic
reasoning is, illusory.
As theories of self-regulation emphasize human agency, they
touch on the issue of free will. Proponents of the automaticity paradigm claim that determinism is the only defensible scientific attitude, and that only their version of situationism is compatible with
it. In contrast, I believe that there is enough evidence for the view
that self-regulation can occur in a deterministic frame. The behav-
Fig. 2. Odysseus as strategic self-regulator.
133
ior of living things and other complex systems is often not fully
predictable even when all external stimuli are known (Gleick,
1987). Self-organization may be chaotic and nonlinear rather than
causal and linear (Prigogine, 1997); yet, it can be fully deterministic. Endorsement of determinism does not entail endorsement of
situationism or even causality (Krueger, 2003; Krueger & Acevedo,
2005; Russell, 1913).
7. Misplaced causal schemas
It is now time to return to the question of why many psychologists cling to the idea that situation and person effects
compete for a fixed pool of behavioral variance. Perhaps they
construe their work as part of a competition between social
and personality psychology. I entertain a different view, one
found in a prominent research area of social psychology itself,
namely, the study of everyday causal reasoning. Heider (1958)
and others (Jones & Davis, 1965; Kelley, 1967) observed that folk
psychology is not concerned with the design of experiments and
the prediction of behavior, but with the explanation of individual
acts once they have occurred. These theorists identified the separation of person effects from situation effects as folk psychology’s principal inference task (but see Malle, Knobe, & Nelson,
2007, for a revisionist account). Kelley (1972a) introduced the
discounting principle as the cleaver that achieves this separation.
According to the discounting principle a potential cause becomes
less probable once another potential cause is introduced. The
principle applies when causes are assumed to be multiply sufficient or, and this is true by definition, when they are assumed to
be compensatory (Kelley, 1972b). The outcome of discounting
can only be as valid as the schema to which it is applied (McClure, 1998).
Much as the quantitative constraints of the componential model can be examined in the context of prediction, so can the constraints on discounting be examined in the context of causal
inference. Morris and Larrick (1995) showed that discounting is
strong inasmuch as a cause’s base rate is low relative
the base
to pðBjSÞ
rate of an alternative cause, inasmuch as its power pðBjPÞ
is low,
or inasmuch as it is negatively correlated with an alternative cause.
The assumption of prepotent situation effects and discountable
person effects may therefore be correct inasmuch as these conditions are met.
pðSÞ
, and the assoTo illustrate the effects of the base rate ratio, pðPÞ
ciation between causes consider three scenarios. In the first scenario, S and P are independent, p(S) ranges from .05 to .95, p(P)
pðSÞ
ranges from 1.05 to 19.
ranges from .05 to p(S) .05, and thus pðPÞ
Both causes are singly sufficient (i.e., p(B|S) = p(B|P) = 1). Discounting is 1 p(S|B) and 1 p(P|B), respectively, for situation and person effects. Fig. 3 (top) shows the logarithmic increase in
discounting P as the base rate ratio becomes more skewed against
P. At the same time, the cause with the higher base rate, S, is discounted less. The center rows in the bottom panel show the average discounting effect and its predictability from the base rate
ratio. The second scenario (top rows in bottom panel) assumes
the most positive correlation between S and P for each base rate ratio, and the third scenario (bottom rows in panel) assumes the
most negative correlation.5 Discounting increases as the correlation
between causes decreases, and the base rate ratio predicts discounting best when the causes are independent.
The most notable implication of this analysis is that the impact
of causal non-independence is much smaller than the impact of the
5
Data for the second scenario were constructed by setting pðP\ SÞ to .001, and
data for the third scenario were constructed by setting pð P\ SÞ to .001 if
pðPÞ P pð SÞ and by otherwise setting pðP \ SÞ to .001. With one degree of freedom,
the other three conjoint probabilities were constrained by the base rates.
134
J.I. Krueger / Journal of Research in Personality 43 (2009) 127–136
Fig. 3. Discounting of independent causes (scatterplot) and discounting of positively correlated, independent, and negatively correlated causes (tables).
base rate ratio6 Whenever researchers express high confidence in
the idea that evidence for one cause permits radical discounting of
the other, the base rates of these causes would already have to be
highly asymmetric. If this is so, then empirical evidence for the presence of the alternative cause is not much of a discovery. It appears
that research conducted from the situationist perspective has not
emancipated itself from the very folk psychological ideas it seeks
to refute. Ordinary perceivers are charged with ‘‘missing causal
schemas” (Nisbett & Ross, 1980, p. 130), while researchers themselves do not know how to justify their own choices among available
schemas. As a consequence, many researchers underestimate the rational grounds of their participants’ responses and they overestimate
what they have learned about the true causes of behavior (Krueger &
Funder, 2004).
6
The compensatory schema is a special case of the multiple-sufficient-causes
schema, and the latter radically constrains the former. The compensatory schema
assumes that the correlation between causes is 1 (or something close to it), but this
U = 1 depends on the base rates of S and B being the same. When base rates differ,
ceilings for positive U range from -.09 to .9 and the floors for negative U range from
.09 to .9 (using base rate probabilities with two decimal points and making the
smallest cell .001).
8. Conclusions
Cronbach (1957, p. 674) famously deplored the separation of
and the antagonism between experimental and correlational psychology. ‘‘The correlational psychologist,” he wrote, ‘‘is in love with
just those variables the experimenter left home to forget [. . .]. Just
as individual variation is a source of embarrassment to the experimenter, so treatment variation attenuates the results of the correlator”. Reinforcing Cronbach’s view, the present analysis suggests
that strict situationism, like strict dispositionism, is an indefensible
scientific stance. The idea that situation and person effects are
compensatory, and that the strength of one effect can be estimated
by subtracting the strength of the other is a heuristic that requires
more justification than it typically receives. Investigators who are
interested in situation effects should exercise caution before claiming that the situation overwhelms the person.
The answer to some of these problems lies again with Cronbach.
The componential model, which I derived from his 1955 article on
unpacking (dis)similarity scores, is one way of separating the scientific project of finding the causes of behavior from folk psychological contamination. The componential model calls for the
study of multiple people who are observed in multiple situations.
J.I. Krueger / Journal of Research in Personality 43 (2009) 127–136
The model reinforces what should be intuitively obvious. A particular situation accounts for behavior inasmuch as the rate of behavior is different in this situation than it is in others. A particular
personality accounts for behavior inasmuch as the person’s rate
of behavior is different from the rate of others. Situations and persons interact in the generation of behavior in as much as the magnitude of the personal differences in one situation differs from
those in others. The componential model seeks to overcome partisan situationism, dispositionism, and interactionism by showing
that all three effects must be considered simultaneously.
A research agenda derived from the componential model faces
significant challenges. Studying multiple people in multiple situations states, but does not solve, the two critical sampling issues.
Sampling respondents is usually driven by convenience (i.e., easy
access to college students). Hence, the reference population is ill
defined. A fortiori, sampling of situations is complicated by a lack
of a generally accepted taxonomy of situations. Yet, some progress
is made when researchers settle on a fixed set of situations and
conduct multiple studies with a known population of participants.
Within such an admittedly non-exhaustive context, the interplay
of the three component causes of behavior can then be studied
(Fleeson, 2007; Letzring, Wells, & Funder, 2006; Wright, Lindgren,
& Zakriski, 2001).
Acknowledgments
This article was completed with generous support from the
Alexander von Humboldt Foundation, while I was a visiting professor at the University of Marburg, Germany. Alexandra Freund educated me on current trends in self-regulation research. Anthony
Evans and Sophie Lebrecht shaped my thinking about situationism
in many stimulating discussions. Leslie Welch enlightened me
about ANOVA. Brent Donnellan, David Funder, and Daniel Leising
offered many stimulating insights, which considerably improved
this article. All remaining errors are the author’s.
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